Encoding of line pattern representation

ABSTRACT

The encoding of a line pattern representation. The line pattern representation has a changing value in a first dimension as a function of a value in a second dimension. The line pattern representation is segmented into multiple segments along the second dimension. The line pattern representation is then encoded by assigning a quantized value to each of the segments based on the changing value of the line pattern in the first dimension as present within the corresponding segment. If the line pattern generally falls within a given range within a segment, the segment will be assigned a quantized value corresponding to that range. The encoding may be used to assign the line pattern representation into a category.

BACKGROUND

The information age is characterized by the widespread availability ofinformation made possible through network communication. However, themass of available information often makes it difficult to extract dataof interest. Because of the potentially laborious nature of extractingvaluable data from large amounts of less valuable information, the laboris often referred to as “data mining”. Less valuable or irrelevantinformation is analogous to raw earth that must be sifted through inorder to find valuable minerals, which are analogous to relevantinformation.

One way to extract information is to submit queries on databases. Thismethod lends itself well to data that has identified properties that aremonitored by the database. However, there is a wide variety of ways inwhich data can be stored. Some types of data, such as time seriescharts, are not quite as easy to sift through as they can oftenrepresent complex line representations that do not lend themselves wellsubject to database queries.

BRIEF SUMMARY

At least some embodiments described herein relate to the encoding of aline pattern representation. The encoding may be helpful when, forexample, categorizing the line pattern representation. The line patternrepresentation has a changing value in a first dimension as a functionof a value in a second dimension. The line pattern representation issegmented into multiple segments along the second dimension. The linepattern representation is then encoded by assigning a quantized value toeach of the segments based on the changing value of the line pattern inthe first dimension as present within the corresponding segment. Forinstance, the line pattern representation may also be divided intomultiple ranges along the first dimension. If the line pattern generallyfalls within a given range (e.g., if the mean of the line pattern iswithin the given range) within a segment, the segment will be assigned aquantized value corresponding to that given range.

At least some embodiments described herein use the encoding to assignthe line pattern representation into a category. For instance, perhapsthose line pattern representations that have the same encodedrepresentation are assigned to the same category. If there are too manycategories, the number of segments in the second dimension and/or thenumber of ranges in the first dimension may be reduced. If there are toofew categories, the number of segments in the second dimension and/orthe number of ranges in the first dimension may be reduced.

This Summary is not intended to identify key features or essentialfeatures of the claimed subject matter, nor is it intended to be used asan aid in determining the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features can be obtained, a more particular descriptionof various embodiments will be rendered by reference to the appendeddrawings. Understanding that these drawings depict only sampleembodiments and are not therefore to be considered to be limiting of thescope of the invention, the embodiments will be described and explainedwith additional specificity and detail through the use of theaccompanying drawings in which:

FIG. 1 abstractly illustrates a computing system in which someembodiments described herein may be employed;

FIG. 2 illustrates a system that encodes each of multiple line patternrepresentations, and uses the encoding to categorize the line patternrepresentations in accordance with the principles described herein;

FIG. 3 illustrates a flowchart of a method for encoding line patternrepresentations and categorizing the line pattern representations basedon the encoding in accordance with the principles described herein;

FIG. 4 illustrates a number of example line pattern representations usedas a specific example of how the principles described herein may beemployed;

FIG. 5 illustrates the line pattern representations of FIG. 4 segmentedinto four segments and ranged into four ranges;

FIG. 6 illustrates the line pattern representations of FIG. 4 segmentedinto eight segments and ranged into eight ranges; and

FIG. 7 illustrates the line pattern representations of FIG. 4 segmentedinto two segments and ranged into two ranges.

DETAILED DESCRIPTION

At least some embodiments described herein relate to the encoding of aline pattern representation. The encoding may be helpful when, forexample, categorizing the line pattern representation. The line patternrepresentation has a changing value in a first dimension (e.g., alongthe vertical or “y” axis) as a function of a value in a second dimension(e.g., along the horizontal or “x” axis). The line patternrepresentation is segmented into multiple segments along the seconddimension. The line pattern representation is then encoded by assigninga quantized value to each of the segments based on the changing value ofthe line pattern in the first dimension as present within thecorresponding segment. For instance, the line pattern representation mayalso be divided into multiple ranges along the first dimension. If theline pattern generally falls within a given range (e.g., if the mean ofthe line pattern is within the given range) within a segment, thesegment will be assigned a quantized value corresponding to that givenrange.

At least some embodiments described herein use the encoding to assignthe line pattern representation into a category. For instance, perhapsthose line pattern representations that have the same encodedrepresentation are assigned to the same category. If there are too manycategories, the number of segments in the second dimension and/or thenumber of ranges in the first dimension may be reduced. If there are toofew categories, the number of segments in the second dimension and/orthe number of ranges in the first dimension may be reduced.

This mechanism for encoding and categorizing line patterns may bequickly performed and thus allows a computing system to quickly operateto categorize or re-categorize large volumes of line representations.While the categorization may not be exact, the categorization is fastand will likely be accurate enough to be able to derive intuitiveinformation from the categorization. Thus, valuable information may bemined from a large number of line pattern representations. Someintroductory discussion of a computing system will be described withrespect to FIG. 1. Then, the encoding and categorization of the linepattern representations will be described with respect to subsequentfigures.

Computing systems are now increasingly taking a wide variety of forms.Computing systems may, for example, be handheld devices, appliances,laptop computers, desktop computers, mainframes, distributed computingsystems, or even devices that have not conventionally been considered acomputing system. In this description and in the claims, the term“computing system” is defined broadly as including any device or system(or combination thereof) that includes at least one physical andtangible processor, and a physical and tangible memory capable of havingthereon computer-executable instructions that may be executed by theprocessor. The memory may take any form and may depend on the nature andform of the computing system. A computing system may be distributed overa network environment and may include multiple constituent computingsystems.

As illustrated in FIG. 1, in its most basic configuration, a computingsystem 100 typically includes at least one processing unit 102 andmemory 104. The memory 104 may be physical system memory, which may bevolatile, non-volatile, or some combination of the two. The term“memory” may also be used herein to refer to non-volatile mass storagesuch as physical storage media. If the computing system is distributed,the processing, memory and/or storage capability may be distributed aswell. As used herein, the term “executable module” or “executablecomponent” can refer to software objects, routings, or methods that maybe executed on the computing system. The different components, modules,engines, and services described herein may be implemented as objects orprocesses that execute on the computing system (e.g., as separatethreads).

In the description that follows, embodiments are described withreference to acts that are performed by one or more computing systems.If such acts are implemented in software, one or more processors of theassociated computing system that performs the act direct the operationof the computing system in response to having executedcomputer-executable instructions. For example, such computer-executableinstructions may be embodied on one or more computer-readable media thatform a computer program product. An example of such an operationinvolves the manipulation of data. The computer-executable instructions(and the manipulated data) may be stored in the memory 104 of thecomputing system 100. Computing system 100 may also containcommunication channels 108 that allow the computing system 100 tocommunicate with other message processors over, for example, network110. The computing system 100 also includes a display, which may be usedto display visual representations to a user.

Embodiments described herein may comprise or utilize a special purposeor general-purpose computer including computer hardware, such as, forexample, one or more processors and system memory, as discussed ingreater detail below. Embodiments described herein also include physicaland other computer-readable media for carrying or storingcomputer-executable instructions and/or data structures. Suchcomputer-readable media can be any available media that can be accessedby a general purpose or special purpose computer system.Computer-readable media that store computer-executable instructions arephysical storage media. Computer-readable media that carrycomputer-executable instructions are transmission media. Thus, by way ofexample, and not limitation, embodiments of the invention can compriseat least two distinctly different kinds of computer-readable media:computer storage media and transmission media.

Computer storage media includes RAM, ROM, EEPROM, CD-ROM or otheroptical disk storage, magnetic disk storage or other magnetic storagedevices, or any other medium which can be used to store desired programcode means in the form of computer-executable instructions or datastructures and which can be accessed by a general purpose or specialpurpose computer.

A “network” is defined as one or more data links that enable thetransport of electronic data between computer systems and/or modulesand/or other electronic devices. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or a combination of hardwired or wireless) to acomputer, the computer properly views the connection as a transmissionmedium. Transmissions media can include a network and/or data linkswhich can be used to carry or desired program code means in the form ofcomputer-executable instructions or data structures and which can beaccessed by a general purpose or special purpose computer. Combinationsof the above should also be included within the scope ofcomputer-readable media.

Further, upon reaching various computer system components, program codemeans in the form of computer-executable instructions or data structurescan be transferred automatically from transmission media to computerstorage media (or vice versa). For example, computer-executableinstructions or data structures received over a network or data link canbe buffered in RAM within a network interface module (e.g., a “NIC”),and then eventually transferred to computer system RAM and/or to lessvolatile computer storage media at a computer system. Thus, it should beunderstood that computer storage media can be included in computersystem components that also (or even primarily) utilize transmissionmedia.

Computer-executable instructions comprise, for example, instructions anddata which, when executed at a processor, cause a general purposecomputer, special purpose computer, or special purpose processing deviceto perform a certain function or group of functions. The computerexecutable instructions may be, for example, binaries, intermediateformat instructions such as assembly language, or even source code.Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above.Rather, the described features and acts are disclosed as example formsof implementing the claims.

Those skilled in the art will appreciate that the invention may bepracticed in network computing environments with many types of computersystem configurations, including, personal computers, desktop computers,laptop computers, message processors, hand-held devices, multi-processorsystems, microprocessor-based or programmable consumer electronics,network PCs, minicomputers, mainframe computers, mobile telephones,PDAs, pagers, routers, switches, and the like. The invention may also bepracticed in distributed system environments where local and remotecomputer systems, which are linked (either by hardwired data links,wireless data links, or by a combination of hardwired and wireless datalinks) through a network, both perform tasks. In a distributed systemenvironment, program modules may be located in both local and remotememory storage devices.

FIG. 2 illustrates a system 200 that encodes each of multiple linepattern representations, and uses the encoding to categorize the linepattern representations. FIG. 3 illustrates a flowchart of a method 300for encoding line pattern representations and categorizing the linepattern representations based on the encoding. As the method 300 of FIG.3 may be performed by the system 200 of FIG. 2, the description of FIGS.2 and 3 will now proceed in an intermingled fashion. The method 300 maybe performed for each of multiple line pattern representations.

The system 200 includes a pattern generation component 201, whichgenerates one or more line pattern representations (act 301 in FIG. 3),each representing a line pattern having a changing value in the firstdimension as a function of a value in a second dimension. The linepattern representation may be generated based on underlying dataaccessible (either locally or remotely) to the pattern generationcomponent 201. Examples of line patterns include, for example, timeseries charts, log series data, usage charts, activity charts, and soforth. As very specific examples, such charts might allow a user toquickly evaluate any type of information such as example call patterns,data center operations, social media response (e.g., number of tweets)regarding a particular actor before and after an academy award event,and so forth.

In FIG. 2, the pattern generation component 201 generates (asrepresented by arrow 221) a set 211 of line pattern representationsrepresented symbolically as A through J in FIG. 2. Although 10 linepattern representations A though J are illustrated in FIG. 2, theellipses K symbolically represents that there is no limit to the numberof line pattern representations generated by the pattern generationcomponent 201 in a single group categorization of line patternrepresentations. There may be up to thousands or even millions or moreof line pattern representations. As previously mentioned, one of theadvantages of embodiments described herein is the ability to encode andquickly categorize large numbers of line pattern representations in arelatively short period of time to enable more real time categorizationof large line pattern data sets.

FIG. 4 illustrates example line pattern representations A through J infurther detail including corresponding represented line pattern 400Athrough 400J. These line pattern representations will be referred to asa single example, although the principles described herein areapplicable to any set of line pattern representations regardless of theline patterns themselves, and regardless of the number of line patternrepresentations. Nevertheless, the example line pattern representationsA though J of FIG. 4 will be a helpful and specific study that willilluminate the more general principles that are not limited to thisexample. In the case of FIG. 4, the first dimension (along which theline pattern value varies) is the vertical dimension often referred toas the “y axis”, whereas the second dimension (representing the inputvalue) is the horizontal axis often referred to as the “x axis”.

Referring again to FIG. 2, the segmentation component 202 accesses theline pattern representations (as represented by arrow 222) in FIG. 2,and segments each the line pattern representation into multiple segmentsalong the second dimension (reference act 302 of FIG. 3). Thesegmentation component 202 also may divide the first dimension of eachline pattern representation into multiple ranges (reference act 303 ofFIG. 3). For instance, FIG. 5 illustrates the same line patternrepresentations A though J of FIG. 4, except that the line patternrepresentations are shown as segmented and ranged. In the case of FIG.5, there are four segments 1 through 4 and four ranges “a” through “d”,although other segmentation and range examples of the line patternrepresentations A through J will be described with respect to FIGS. 6and 7.

The encoding component 203 access the segmented and ranged line patternrepresentation (as represented by arrow 223) in FIG. 2, and assigns aquantized value to each of the segments for each of the line patternrepresentations based on the changing value in the first dimension aspresent within the corresponding segment (reference act 304 of FIG. 3).For instance, in FIG. 5, the line pattern of line pattern representationA has a value that generally falls within range “b” within segment 1,within range “b” within segment 2, within range “c” within segment 3,and within range “c” for segment 4. Accordingly, the line patternrepresentation might be encoded with the sequence “bbcc”, labeling theapplicable ranges from left to right as the line pattern moves throughthe segments 1 through 4 in order.

The assignment of the range within which the line pattern falls for agiven segment may be a relatively straightforward calculation in orderto allow the categorization process to be efficient so that even largedata sets may be quickly categorized. As an example, the mean of theline pattern within the corresponding segment may be calculated, and theidentifier for the range within which that mean falls will be assignedfor that segment. However, the principles described herein are notlimited to how the range identifier for any given segment is identified.

As for the line pattern of line pattern representation B in FIG. 5, themean of the line pattern falls within the range “a” within the segment1, within the range “b” within the segment 2, within the range “c”within the segment 3, and within range “d” within the segment 4. Thus,the line pattern representation B is encoded with the sequence “abcd”(for reader reference, a dot is placed in the upper left corner of theapplicable range for each segment of each line pattern representation Athrough J shown in FIGS. 5 through 7). This may continue for all linepattern representations A through J of FIG. 5 to encode the linepatterns as shown in the following Table 1:

TABLE 1 Line Pattern Representation Encoded Identifier Representation Abbcc B abcd C aacd D bbcc E bbcc F aadd G abcd H aacd I abcd J bbcc

A pattern categorization component 204 accesses the encodedrepresentations (as represented by arrow 224), and categorizes each ofthe line pattern representations using the encoded representations. Forinstance, the line pattern representations may be assigned (asrepresented by arrow 225) a category 212 such that a category isassigned to each unique encoded representation. Thus, those of the linepattern representations that have the same encoded representation areassigned to the same category.

In this specific example, there are four resulting unique encodedvalues, and thus there are three categories 212A through 212D. Thoseline pattern representations that have encoding “bbcc” (line patternrepresentations, A, D, E and J) are assigned into category 212A. Thoseline pattern representations that have encoding “abcd” (line patternrepresentations B, G and I) are assigned into category 212B. Those linepattern representations that have encoding “aacd” (line patternrepresentations C and H) are assigned into category 212C. Those linepattern representations that have encoding “aadd” (only line patternrepresentation F) are assigned to category 212D. Just like there may bemore line pattern representations not illustrated in FIG. 2 asrepresented by ellipses K, there may be more categories as representedby ellipses 212E, and there may be more line pattern representations inthe illustrated categories 212A through 212D also.

While all of the line pattern representations generated by the patterngeneration component 201 may be converted into an encoding and therebyassigned to a category, that need not be the case. For whatever reason,perhaps not all line pattern representations that are generated (asrepresented by arrow 221) might segmented. Furthermore, perhaps not allline pattern representations that are segmented are encoded. Finally,perhaps not all line pattern representations that are encoded areassigned to a category.

In any case, the segmentation component 202 may make differentdeterminations as to how to segment and range the various line patternrepresentations. For instance, FIG. 6 illustrates that the segmentationcomponent segments the line pattern representations into eight differentsegments and ranges the line pattern representation into ranges labeled“a” through “h”. The encoding result would be an eight member sequencewhere each member may be anywhere from “a” to “h” inclusive, dependingon the value of the line pattern within the corresponding segment.Accordingly, the encoding component 203 would assign the encoding valuesillustrated in the following Table 2.

TABLE 2 Line Pattern Representation Encoded Identifier Representation Accddeeff B abcdeggh C aaabdggh D ccddeeff E ccddeeef F aaabghhh Gabcdeggh H ababeefh I bccdefhh J cddcefff

In this case, there are a larger number of unique encodings. Forinstance, there are eight unique encodings resulting in 10 line patternrepresentations. Thus, there are 8 categories with only two categorieshaving multiple line pattern representations. For instance, encodingccddeeff corresponds to a category that includes two line patternrepresentations A and D. Encoding abcdeggh corresponds to a categorythat includes two line pattern representations B and G. Thus, as thesegmentation and ranging granularity increased, line patternrepresentations that are within a single category are more closelymatched, but there tend to be fewer numbers of line patternrepresentations per category.

As another example that moves in the opposite level of granularity, FIG.7 illustrates that the segmentation component segments the line patternrepresentations into only two segments and ranges the line patternrepresentation into two ranges labeled “a” through “b”. The encodingresult would be a two member sequence where each member has only one oftwo possible values “a” and “b”, depending on the value of the linepattern within the corresponding segment. Accordingly, the encodingcomponent 203 would assign the encoding values illustrated in thefollowing Table 3.

TABLE 3 Line Pattern Representation Encoded Identifier Representation Aab B ab C ab D ab E ab F ab G ab H ab I ab J ab

In this case, there is one unique encoding, and thus one category, inwhich all ten line pattern representations belong. For most people andapplications, for the data set example of FIG. 4, the segmentationgranularity of Table 1 might likely be the most helpful. However, theoptimal level of granularity is fact dependent, and may depend onsubjective factors such as user preferences. There may be an optimalsweet spot of granularity that most effectively communicates data to agiven user given the surrounding circumstances.

The effective level of granularity may be found at least in part in anautomated fashion using a segmentation adjustment component 231. In oneexample, before or after visualizing the line pattern categories 212 ona display (such as display 112), the segmentation adjustment component231 might analyze the number of categories that resulted from a givenlevel of segmentation in one dimension and ranging in the otherdimension. If the segmentation adjustment component 231 decides that thenumber of categories is just too high, then the segmentation adjustmentcomponent 231 decreases the number of segments and/or ranges. If thesegmentation adjustment component 231 decides that the number ofcategories is just too low, then the segmentation adjustment component231 increases the number of segments or ranges. This may iterate anumber of times until the segmentation adjustment component 231estimates an ideal level of segmentation.

The segmentation adjustment component 231 may operate entirely inresponse to user input such that the user drives all re-segmentation andranging in real time, and/or in response to registered user preferences.Alternatively, the segmentation adjustment component 231 may operateentirely in an automated fashion. The segmentation adjustment component231 may also consider previous user adjustment made when presentingcategories of line pattern representations to the user. Any othercontextual factors might also be considered such as time of day, age ofuser, size of the line representation data set.

In one embodiment, user themself may have provided one of the linepattern representations. As categories are formed, those line patternsthat match the same category as the line pattern representation providedby the user may be considered to be the line patterns that most closelyalign with the users inputted line pattern representation. Thus,searches of line pattern representations based on user input may also beefficiently performed.

Autocomplete of line pattern representations may also be performed byrepeatedly performing a search operation (by categorizing the input linepattern representation made to that point, and by also categorizing thedata set line pattern representations also to the same point), andfinding those line patterns that to that point. As the user draws aninput line pattern, the number of matching line pattern representationswould decrease. Once the user found an acceptable or search form linepattern representation within the data set, the user might simply selectthat visualization of the line pattern representation.

Accordingly, an efficient mechanism for encoding and categorizing linepattern representations has been described. The present invention may beembodied in other specific forms without departing from its spirit oressential characteristics. The described embodiments are to beconsidered in all respects only as illustrative and not restrictive. Thescope of the invention is, therefore, indicated by the appended claimsrather than by the foregoing description. All changes which come withinthe meaning and range of equivalency of the claims are to be embracedwithin their scope.

What is claimed is:
 1. A system comprising: a pattern generationcomponent configured to provide a line pattern representationrepresenting a line pattern having a changing value in a first dimensionas a function of a value in a second dimension; a segmentation componentconfigured to segment the line pattern representation into a pluralityof segments along the second dimension; an encoding component configuredto encode the line pattern representation into an encoded representationby assigning a quantized value to each of the plurality of segmentsbased on the changing value in the first dimension as present within thecorresponding segment; a pattern categorization component configured tocategorize each line pattern representation into a category of aplurality of categories using the encoded representation; and asegmentation adjustment component configured to adjust at least a numberof the plurality of segments that the segmentation component segments,wherein the segmentation adjustment component is configured to at leastadjust at least the number of the plurality of segments as a function ofa number of the plurality of categories.
 2. The system in accordancewith claim 1, wherein the pattern generation component is configured toprovide a first plurality of line pattern representations, eachrepresenting a line pattern having a changing value in a first dimensionas a function of a value in a second dimension, the segmentationcomponent is configured to segment the line pattern representation foreach of a second plurality of line pattern representations of the firstplurality of line pattern representations into a plurality of segmentsalong the second dimension, and the encoding component is configured toencode a line pattern representation for each of a third plurality ofline pattern representations of the plurality of segments into theencoded representation by assigning a quantized value to each of theplurality of segments based on the changing value in the first dimensionas present within the corresponding segment.
 3. The system in accordancewith claim 2, wherein the pattern categorization component is configuredto categorize each of at least a fourth plurality of line patternrepresentations of the third plurality of line pattern representationsinto the plurality of categories using the encoded representation ofeach of the fourth plurality of line pattern representations.
 4. Thesystem in accordance with claim 3, wherein the pattern categorizationcomponent assigns the category to each unique encoded representationsuch that those of the third plurality of line pattern representationsthat have the same encoded representation are in the same category. 5.The system in accordance with claim 2, wherein the segmentationadjustment component adjusts at least the number of the plurality ofsegments that the segmentation component segments the second pluralityof line pattern representations into.
 6. The system in accordance withclaim 5, wherein the adjustment is performed in response, at least inpart, to user input.
 7. The system in accordance with claim 5, whereinthe adjustment is performed automatically by the segmentation adjustmentcomponent.
 8. The system in accordance with claim 7, wherein thesegmentation adjustment component adjusts at least the number of theplurality of segments, at least in part, as the function of the numberof the plurality of categories.
 9. The system in accordance with claim5, wherein the segmentation adjustment component further adjusts a levelof quantization of the quantized values that the encoding componentassigns to each of the plurality of segments.
 10. A computer programproduct comprising one or more computer-readable storage media deviceshaving stored thereon computer-executable instructions that whenexecuted by one or more processors of a computing system, cause thecomputing system to perform a method comprising: an act of providing aline pattern representation representing a line pattern having achanging value in a first dimension as a function of a value in a seconddimension; an act of segmenting the line pattern representation into aplurality of segments along the second dimension; an act of encoding theline pattern representation into an encoded representation by assigninga quantized value to each of the plurality of segments based on thechanging value in the first dimension as present within thecorresponding segment; an act of categorizing each line patternrepresentation into a category of a plurality of categories using theencoded representation; and adjusting at least a number of the pluralityof segments that the act of segmenting segments as a function of anumber of the plurality of categories.
 11. The computer program productin accordance with claim 10, the method further comprising: an act ofdetermining a number of the plurality of segments to segment the linepattern representation into in the act of segmenting.
 12. The computerprogram product in accordance with claim 11, wherein the act ofdetermining is performed in response to user input.
 13. The computerprogram product in accordance with claim 10, wherein the act ofproviding is included as part of an act of providing a first pluralityof line pattern representations, each representing a line pattern havinga changing value in a first dimension as a function of a value in asecond dimension, the act of segmenting is included as part of an act ofsegmenting the line pattern representation for each of a secondplurality of line pattern representations of the first plurality of linepattern representations into a plurality of segments along the seconddimension, and the act of encoding is included as part of an act ofencoding a line pattern representation for each of a third plurality ofline pattern representations of the plurality of segments into anencoded representation by assigning a quantized value to each of theplurality of segments based on the changing value in the first dimensionas present within the corresponding segment.
 14. The computer programproduct in accordance with claim 13, the method further comprising: anact of categorizing each of at least a fourth plurality of line patternrepresentations of the third plurality of line pattern representationsinto the plurality of categories using the encoded representation ofeach of the fourth plurality of line pattern representations.
 15. Thecomputer program product in accordance with claim 10, wherein the firstdimension is a vertical display dimension, and the second dimension is ahorizontal display dimension.
 16. The computer program product inaccordance with claim 10, wherein the line pattern representation is aportion of a larger line pattern representation.
 17. The computerprogram product in accordance with claim 10, wherein the line patternrepresentation is input by a user.
 18. The computer program product inaccordance with claim 10, wherein the line pattern representation is atime series representation.
 19. The computer program product inaccordance with claim 10, wherein the line pattern representation is alog series representation.
 20. A method for categorizing a plurality ofline pattern representations into a plurality of categories, the methodcomprising: an act of providing a first plurality of line patternrepresentations, each representing a line pattern having a changingvalue in a first dimension as a function of a value in a seconddimension; an act of segmenting the line pattern representation for eachof a second plurality of line pattern representations of the firstplurality of line pattern representations into a plurality of segmentsalong the second dimension; and an act of encoding a line patternrepresentation for each of a third plurality of line patternrepresentations of the plurality of segments into an encodedrepresentation by assigning a quantized value to each of the pluralityof segments based on the changing value in the first dimension aspresent within the corresponding segment; and an act of categorizingeach of at least a fourth plurality of line pattern representations ofthe third plurality of line pattern representations into a plurality ofcategories using the encoded representation of each of the fourthplurality of line pattern representations; and an act of adjusting atleast a number of the plurality of segments as a function of a number ofthe plurality of categories.